Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytic goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist analysts throughout the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance.

In this work, we establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.

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The Interactive Music Mapping Vienna (IMMV) research project has been featured in a few newspapers and online magazines, including 'Die Presse', 'Der Standard', and FWF's 'Scilog'. The project’s focus is the valorisation and the mediation of the capabilities of music as an urban identification tool.

As a result of a major restructuring of the Faculty of Informatics, we are announcing that our workgroup has transitioned to the Institute of Visual Computing & Human-Centered Technology, starting with 01.01.2018.